# Create RKNN object rknn = RKNN() # Set model config print('--> Config model') rknn.config(mean_values=[[127.5, 127.5, 127.5]], std_values=[[127.5, 127.5, 127.5]], reorder_channel='0 1 2', batch_size=16) print('done') # Hybrid quantization step2 print('--> hybrid_quantization_step2') ret = rknn.hybrid_quantization_step2( model_input='./ssd_mobilenet_v2.json', data_input='./ssd_mobilenet_v2.data', model_quantization_cfg='./ssd_mobilenet_v2.quantization.cfg', dataset='./dataset.txt') if ret != 0: print('hybrid_quantization_step2 failed!') exit(ret) print('done') # Export RKNN model print('--> Export RKNN model') ret = rknn.export_rknn('./ssd_mobilenet_v2.rknn') if ret != 0: print('Export RKNN model failed!') exit(ret) print('done')
from rknn.api import RKNN if __name__ == '__main__': # Create RKNN object rknn = RKNN() # Set model config print('--> config model') rknn.config(channel_mean_value='123.675 116.28 103.53 58.395', reorder_channel='0 1 2') print('done') # Hybrid quantization step2 print('--> hybrid_quantization_step2') ret = rknn.hybrid_quantization_step2(model_input='./mnasnet0_5.json', data_input='./mnasnet0_5.data', model_quantization_cfg='./mnasnet0_5.quantization.cfg', dataset='./dataset.txt') if ret != 0: print('hybrid_quantization_step2 failed!') exit(ret) print('done') # Export RKNN model print('--> Export RKNN model') ret = rknn.export_rknn('./mnasnet0_5.rknn') if ret != 0: print('Export RKNN model failed!') exit(ret) print('done') rknn.release()